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The safety and robustness of the network have attracted the attention ofpeople from all walks of life, and the damage of several key nodes will lead toextremely serious consequences. In this paper, we proposed the clusteringH-index mixing (CHM) centrality based on the H- index of the node itself andthe relative distance of its neighbors. Starting from the node itself andcombining with the topology around the node, the importance of the node and itsspreading capability were determined. In order to evaluate the performance ofthe proposed method, we use Susceptible-Infected-Recovered (SIR) model,monotonicity and resolution as the evaluation standard of experiment.Experimental results in artificial networks and real-world networks show thatCHM centrality has excellent performance in identifying node importance and itsspreading capability.

Ranking the spreading influence of nodes in complex networks based on mixing degree centrality and local structure